Name	Size	Mode	Actions
__pycache__/	-	0755	rm
conv.py	6326	0644	editdlrm
linear.py	2334	0644	editdlrm
__init__.py	134	0644	editdlrm

import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.intrinsic import LinearReLU class Linear(nn.Linear): r""" A linear module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Linear`, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation. Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to default. Attributes: weight: fake quant module for weight """ _FLOAT_MODULE = nn.Linear def __init__(self, in_features, out_features, bias=True, qconfig=None, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__(in_features, out_features, bias, **factory_kwargs) assert qconfig, 'qconfig must be provided for QAT module' self.qconfig = qconfig self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs) def forward(self, input): return F.linear(input, self.weight_fake_quant(self.weight), self.bias) @classmethod def from_float(cls, mod): r"""Create a qat module from a float module or qparams_dict Args: `mod` a float module, either produced by torch.quantization utilities or directly from user """ assert type(mod) == cls._FLOAT_MODULE, ' qat.' + cls.__name__ + '.from_float only works for ' + \ cls._FLOAT_MODULE.__name__ assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined' assert mod.qconfig, 'Input float module must have a valid qconfig' if type(mod) == LinearReLU: mod = mod[0] qconfig = mod.qconfig qat_linear = cls(mod.in_features, mod.out_features, bias=mod.bias is not None, qconfig=qconfig) qat_linear.weight = mod.weight qat_linear.bias = mod.bias return qat_linear def to_float(self): linear = torch.nn.Linear(self.in_features, self.out_features, self.bias is not None) linear.weight = torch.nn.Parameter(self.weight.detach()) if self.bias is not None: linear.bias = torch.nn.Parameter(self.bias.detach()) linear.train(self.training) return linear